BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 60 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/general/mk_fgraph.m b/sourcecodes/bnt-master/BNT/general/mk_fgraph.m
new file mode 100644
index 00000000..8ac50338
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/general/mk_fgraph.m
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+function fg = mk_fgraph(G, node_sizes, factors, varargin)
+% MK_FGRAPH Make a factor graph
+% fg = mk_fgraph(G, node_sizes, factors, ...)
+%
+% A factor graph is a bipartite graph, with one side containing variables,
+% and the other containing functions of (subsets of) these variables.
+% For details, see "Factor Graphs and the Sum-Product Algorithm",
+%  F. Kschischang and B. Frey and H-A. Loeliger,
+%  IEEE Trans. Info. Theory, 2001
+%
+% G(i,j) = 1 if there is an arc from variable i to factor j
+%
+% node_sizes(i) is the number of values node i can take on,
+%   or the length of node i if i is a continuous-valued vector.
+%
+% 'factors' is the list of factors (kernel functions)
+%
+% The list below gives optional arguments [default value in brackets].
+% 
+% equiv_class - equiv_class(i)=j  means factor node i gets its params from factors{j} [1:F]
+% discrete - the list of nodes which are discrete random variables [1:N]
+%
+% e.g., fg = mk_fgraph(G, [2 2], {bnet.CPD{1},bnet.CPD{2}}, 'discrete', [1 2])
+
+fg.G = G;
+fg.node_sizes = node_sizes;
+fg.factors = factors;
+[fg.nvars fg.nfactors] = size(G);
+
+% default values for parameters
+fg.equiv_class = 1:fg.nfactors;
+fg.dnodes = 1:fg.nvars;
+
+if nargin >= 4
+  args = varargin;
+  nargs = length(args);
+  for i=1:2:nargs
+    switch args{i},
+     case 'equiv_class', fg.equiv_class = args{i+1}; 
+     case 'discrete',    fg.dnodes = args{i+1}; 
+     otherwise,  
+      error(['invalid argument name ' args{i}]);       
+    end
+  end
+end
+
+% so that determine_pot_type will work...
+fg.utility_nodes = [];
+%fg.decision_nodes = [];
+%fg.chance_nodes = fg.nvars;
+
+fg.dom = cell(1, fg.nfactors);
+for f=1:fg.nfactors
+  fg.dom{f} = find(G(:,f));
+end
+fg.dep = cell(1, fg.nvars);
+for x=1:fg.nvars
+  fg.dep{x} = find(G(x,:));
+end
+fg.cnodes = mysetdiff(1:fg.nvars, fg.dnodes);